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{
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"id": "b6948f5d-bf4b-4704-8249-0bfe965bcccc",
"metadata": {},
"outputs": [],
"source": [
"def return_eval(pred2score, target2score, mean):\n",
" mean2 = 2 * mean\n",
" pred = [p.lower() for p in pred2score]\n",
" target = [p.lower() for p in target2score]\n",
" o = len(set(target))\n",
"\n",
" intersect = len(set(pred[:o]).intersection(set(target)))\n",
" budgetaccone = len(set(pred[:mean]).intersection(set(target)))/mean\n",
" budgetacctwo = len(set(pred[:mean2]).intersection(set(target)))/mean2\n",
" prec = intersect/len(set(pred[:o])) if len(pred) > 0 else 0.0\n",
" rec = intersect/len(target)\n",
"\n",
" \n",
" kmean = len(set(pred[:mean]))\n",
" k2mean = len(set(pred[:mean2]))\n",
"\n",
" if prec==0 and rec==0:\n",
" f1=0\n",
" else:\n",
" f1 = 2*prec*rec/(prec+rec)\n",
" \n",
" return {\"P@O\":100*prec, \"R@O\": 100*rec, \"F1@O\":100*f1, \"B@mean\": budgetaccone, \"B@2mean\": budgetacctwo, \"#k@mean\": kmean, \"#k@2mean\": k2mean}\n",
"\n",
"def final_metric_results(preds_keyphrases, labels_keyphrases, mean):\n",
" avg_scores = defaultdict(list)\n",
" for pred, target in zip(preds_keyphrases, labels_keyphrases):\n",
"\n",
" all_exact_results = return_eval(pred, target, mean)\n",
" \n",
" for m_name, value in all_exact_results.items():\n",
" avg_scores[m_name].append(value)\n",
"\n",
" avg_scores[\"pred_kpnum\"].append(len(set(pred)))\n",
" avg_scores[\"gt_kpnum\"].append(len(set(target)))\n",
" \n",
" avg_scores = {m_name: round(np.mean(values),2) for m_name, values in avg_scores.items()}\n",
"\n",
" return avg_scores\n",
" \n",
"def generate_results(df, mean):\n",
" \n",
" labels_keyphrases = [p.lower().split(\";\") for p in df[\"target\"]]\n",
" preds_keyphrases = []\n",
" for i in range(len(df)):\n",
" # preds_keyphrases.append(post_process(df.iloc[i][\"keyword\"])[:k])\n",
" preds_keyphrases.append(post_process(df.iloc[i][\"keyword\"]))\n",
" \n",
" print(\"@\",mean) \n",
" return final_metric_results(preds_keyphrases, labels_keyphrases, mean)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "761906b8-6c1d-4d48-adcf-20589d9a0385",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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